Dryland ecosystems cover 41% of Earth's land surfaces, account for 44% of cultivated lands and 60% of food sources, and make large contributions to the global water and carbon cycles. However, these ecosystems are experiencing unprecedented extremes including heatwaves, floods, and droughts as well as hotter temperatures and often declining water resource availability. These ecosystems are some of the most challenging to monitor given their high temporal variability with rapid response to their environment as well as vast spatial variability with intermixing of different plant species and life forms amongst bare soil coverage. The Adaptation and Response in Drylands (ARID) campaign was selected by National Aeronautics and Space Administration (NASA) as a scoping study to develop a research agenda for a dryland field campaign. Here, we detail our ARID science research agenda and implementation plan that were developed based on an extensive community engagement effort in over 160 events with over a thousand scientists, land managers, and Tribal communities between 2023 and 2024. The selected science themes cover drought and climate variability, ecosystem structure, function, and biodiversity, carbon cycle interannual variability and trends, and social ecological systems (land management and adaptation). We then detail our remote sensing, modeling, and field-based strategies to capture high temporal and high spatial resolution processes. Finally, our implementation strategy is presented which includes focus area selections in a core intensive western U.S. domain and distributed international domains. This strategy includes our overarching guiding principles of using multi-temporal airborne acquisitions and super sites as well as enhancing land management in co-development with end-user partners. While originally developed for NASA, our ARID report creates a blueprint for any future dryland field campaign, at any scale, that can be implemented widely for foundational and applied science objectives.

Abstract The editors of Journal of Advances in Modeling Earth Systems thank the 1,035 reviewers who provided 1,649 reviews during 2025. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.

Drylands cover 41% of Earth's land surface, support 36% of the global population and contribute 60% of global food production. Despite these ecosystems' importance and high vulnerability to droughts and heatwaves, drylands remain some of the most understudied systems on Earth. Monitoring drylands is challenging due to their complex ecosystem structure of visible soil mixed with diverse plant species that respond rapidly to weather and climate. In 2023 and 2024, a NASA scoping study was conducted for a proposed dryland terrestrial ecology field campaign called Adaptation and Response in Drylands (ARID). Thereafter, the NASA ARID scoping team submitted their campaign proposal to NASA Headquarters, providing a study design for how field, aircraft and satellite measurements, as well as modeling, could address the most critical fundamental and applied science questions in drylands. The extensive strategic vision was created by and for the drylands research community, including remote sensors, modelers, experimentalists and ecologists from across the world, and the overall approach can be further utilized and altered for different uses and data information needs. Here, we summarize the final ARID research agenda, including its main objectives, field campaign strategy, data end-user support strategy, and U.S. and global community engagement.

Global terrestrial ecosystems exhibit substantial interannual variability (IAV) in net carbon (C) flux. Determining the biogeographic origin of this variability is essential for the understanding and forecasting of global C cycling and carbon-climate feedbacks. Currently, most studies identify either global drylands or moist tropical forests as the dominant source of IAV. Considering this, we investigated whether the use of three different global ecosystem classifications of drylands and moist tropical forests, as well as two alternative geographical scales, could alter which ecosystem is the dominant contributor to terrestrial net C flux IAV. Using the simulation results of 18 dynamic global vegetation models from the TRENDY v11 model intercomparison, we calculated the absolute and area-weighted contributions of net C flux IAV for: individual 0.5° grid cells, global ecosystem classifications, and ecoregions (intermediate scale between grid cells and global ecosystems). For all three of the global ecosystem classification schemes, we found the drylands IAV contributions of 41%, 32%, and 37% were significantly greater than the associated IAV contributions of 20%, 19%, and 24% from the moist tropical forests ( p < 0.001). However, the moist tropical forests had a higher IAV contribution per unit area across all three classification schemes (∼3% versus ∼1%–2% ( p < 0.001)). At the ecoregion scale, this switch between drylands and moist tropical forests was absent; as seven of the ten highest absolute and nine of the ten highest area-weighted contributing ecoregions were drylands. Specifically, we found tropical and subtropical grasslands, savannas, and shrublands to be particularly substantial contributors to global terrestrial net C flux IAV, with the Cerrado’s absolute IAV contribution of 3.52% exceeding all but one of the other 763 global ecoregions IAV contributions (all p < 0.05). Our findings demonstrate that drylands persist as the dominant contributor to global terrestrial net C flux IAV, irrespective of different global ecosystem classifications or geographic scales.

Accurately predicting terrestrial ecosystem responses to climate change over long-timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin-up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

The editors of Journal of Advances in Modeling Earth Systems thank the 1,001 reviewers who provided 1,593 reviews during 2024. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.

Abstract Savannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

Plain Language SummaryThe editors of Journal of Advances in Modeling Earth Systems thank the 919 reviewers who provided 1,413 reviews during 2023. Their hard work, done anonymously, benefits authors, and readers.

Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements.

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest - but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/-64 gC m -2 yr � 1). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

&lt;p&gt;Drylands have tightly coupled water and carbon cycles due to persistent water scarcity, making them valuable systems for understanding coupled ecohydrological and biogeochemical processes. In addition, dryland ecosystems contribute significantly to the interannual variability of the terrestrial carbon sink. To better characterize dryland carbon dynamics, we present DryFlux, a machine learning upscaled product based on a dense network of eddy covariance sites in the North American Southwest. This product combines in-situ fluxes with remote sensing and meteorological data to estimate gross primary productivity in drylands while explicitly accounting for water limitation during the model development process. DryFlux outperforms existing products in capturing interannual and seasonal variation in carbon uptake when used globally. We specifically explore how machine learning techniques can accurately upscale fluxes at multiple spatial (1 km and 9 km) and temporal (daily, weekly, monthly) scales to find the best resolution for capturing spatial and temporal heterogeneity in carbon and water fluxes. In addition, we discuss how remotely sensed soil moisture from satellites can help capture biogeochemical &#039;hot spots&#039; and &#039;hot moments&#039; in drylands. Our findings can help us better understand dynamic carbon fluxes in drylands, as well as the spatiotemporal resolution needed to resolve water-carbon dynamics in these and other systems. Machine learning methods that explicitly incorporate water limitation in model development can contribute to a more comprehensive understanding of carbon, energy, and water fluxes at multiple scales.&lt;/p&gt;

In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

The editors of Journal of Advances in Modeling Earth Systems thank the 702 reviewers who provided 1362 reviews during 2022. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.

In spite of the importance of land ecosystems in offsetting carbon dioxide emissions released by anthropogenic activities into the atmosphere, the spatiotemporal dynamics of terrestrial carbon fluxes remain largely uncertain at regional to global scales. Over the past decade, data assimilation (DA) techniques have grown in importance for improving these fluxes simulated by terrestrial biosphere models (TBMs), by optimizing model parameter values while also pinpointing possible parameterization deficiencies. Although the joint assimilation of multiple data streams is expected to constrain a wider range of model processes, their actual benefits in terms of reduction in model uncertainty are still under-researched, also given the technical challenges. In this study, we investigated with a consistent DA framework and the ORCHIDEE-LMDz TBM–atmosphere model how the assimilation of different combinations of data streams may result in different regional to global carbon budgets. To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, spaceborne estimates of the normalized difference vegetation index, and atmospheric CO2 concentration data measured at stations) were assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C land fluxes. We found that a major challenge in improving the spatial distribution of the land C sinks and sources with atmospheric CO2 data relates to the correction of the soil carbon imbalance.

The mission of AGU's Journal of Advances in Modeling Earth Systems (JAMES) is to publish original research papers that advance the science underlying Earth system models and emerging from their use. JAMES' scope encompasses the outer envelope of the Earth system including the atmosphere, oceans, land surface, and cryosphere. It publishes papers that expand capabilities to model, understand, and predict the Earth system and the physical, chemical, and biological processes shaping it. In this editorial, we present general principles as well as specific notions that guide the strategy of JAMES' editors in realizing the journal's mission. This document serves as an update to Griffies et al. (2021), https://doi.org/10.1029/2021MS002567.

Predicting terrestrial carbon, C, budgets and carbon‐climate feedbacks strongly relies on our ability to accurately model interactions between vegetation, C and water cycles, and the atmosphere. However, C fluxes simulated by global, process‐based terrestrial biosphere models (TBMs) remain subject to large uncertainties, partly due to unknown or poorly calibrated parameters. This is because TBMs have not routinely been confronted against C cycle related datasets within a statistical data assimilation (DA) system. In this review, we present 15 years' development of a C cycle DA system for optimizing C cycle parameters of the ORCHIDEE TBM. We analyze the impact of assimilating multiple different C cycle related datasets on regional to global‐scale gross and net CO 2 fluxes. We find that assimilating atmospheric CO 2 data is crucial for improving (increasing) ORCHIDEE predictions of the terrestrial land C sink. The improvement is predominantly due to the global‐scale constraint these data provide for optimizing initial soil C stocks, which are likely in error due to inaccurate assumptions about steady state spin‐up and incomplete knowledge of land use change histories. When comparing the data‐constrained ORCHIDEE land C sink estimates to the CAMS atmospheric inversion, we show that while the two approaches agree on the global C sink magnitude, they continue to differ in how the global C sink is partitioned between the northern hemisphere and tropics. We also discuss technical challenges faced in our C cycle DA studies, in particular the difficulty in characterizing the error covariance matrix due to unknown observation biases and/or model‐data inconsistencies. We offer our perspectives on how to tackle these challenges that we hope can serve as a roadmap for other TBM groups wishing to develop C cycle DA systems.

Understanding dryland dynamics is essential to predict future climate trajectories. However, there remains large uncertainty on the extent to which drylands are expanding or greening, the drivers of dryland vegetation shifts, the relative importance of different hydrological processes regulating ecosystem functioning, and the role of land-use changes and climate variability in shaping ecosystem productivity. We review recent advances in the study of dryland productivity and ecosystem function and examine major outstanding debates on dryland responses to environmental changes. We highlight often-neglected uncertainties in the observation and prediction of dryland productivity and elucidate the complexity of dryland dynamics. We suggest prioritizing holistic approaches to dryland management, accounting for the increasing climatic and anthropogenic pressures and the associated uncertainties. In this Review, the authors discuss recent advances in understanding dryland productivity and functions, examining outstanding debates on dryland response to change and the uncertainties associated with predicting climate trajectories.

Carbon, water and energy exchange between the land and atmosphere controls how ecosystems either accelerate or ameliorate the effect of climate change. However, evaluating improvements to processes controlling carbon cycling, water use and energy exchange in global land surface models (LSMs) remains challenging in part because of persistent model errors in estimating leaf area. Here we evaluate the changes in global carbon, water and energy exchange brought about when a LSM prognostic estimates of leaf area are made consistent with estimates from satellites. This approach achieves two aims; first to quantify the effect of ignoring errors in leaf area index (LAI) on land‐atmosphere fluxes and second, to evaluate how closely this LSM replicates fluxes with and without an LAI constraint. We implemented an ensemble Kalman filter with spatiotemporal adaptive inflation to more closely match community land model (CLM5.0) estimates of leaf area to those from the Global Inventory Modeling and Mapping Studies leaf area index (LAI3g) product. We then evaluate the model's estimates of gross primary productivity (GPP) and latent heat flux (LE) against well established global estimates of these fluxes. We find that the model is biased high by 27% relative to the LAI3g product. Moreover, the effect of bias in LAI is substantial for GPP (18%) and LE (6%) and likely to confound efforts to refine processes controlling these fluxes. This data assimilation approach serves as a method to evaluate the efficacy of refinements to flux processes until the processes controlling the dynamics of LAI are better resolved in LSMs.